Keyword spotting accuracy degrades when neural networks are exposed to noisy environments. On-site adaptation to previously unseen noise is crucial to recovering accuracy loss, and on-device learning is required to ensure that the adaptation process happens entirely on the edge device. In this work, we propose a fully on-device domain adaptation system achieving up to 14% accuracy gains over already-robust keyword spotting models. We enable on-device learning with less than 10 kB of memory, using only 100 labeled utterances to recover 5% accuracy after adapting to the complex speech noise. We demonstrate that domain adaptation can be achieved on ultra-low-power microcontrollers with as little as 806 mJ in only 14 s on always-on, battery-operated devices.
@article{arxiv.2403.10549,
title = {On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems},
author = {Cristian Cioflan and Lukas Cavigelli and Manuele Rusci and Miguel de Prado and Luca Benini},
journal= {arXiv preprint arXiv:2403.10549},
year = {2024}
}